By Balaji Krishnapuram,Shipeng Yu,R. Bharat Rao
In computing device studying functions, practitioners needs to bear in mind the cost associated with the set of rules. those expenses contain:
- Cost of buying education data
- Cost of knowledge annotation/labeling and cleaning
- Computational price for version becoming, validation, and testing
- Cost of accumulating features/attributes for try out data
- Cost of consumer suggestions collection
- Cost of unsuitable prediction/classification
Cost-Sensitive computing device Learning is likely one of the first books to supply an outline of the present study efforts and difficulties during this region. It discusses real-world functions that contain the price of studying into the modeling approach.
The first a part of the e-book offers the theoretical underpinnings of cost-sensitive computer studying. It describes well-established desktop studying ways for decreasing facts acquisition expenditures in the course of education in addition to techniques for lowering charges while platforms needs to make predictions for brand spanking new samples. the second one half covers real-world functions that successfully exchange off types of expenditures. those purposes not just use conventional computer studying methods, yet additionally they comprise state-of-the-art learn that advances past the constraining assumptions through interpreting the appliance wishes from first principles.
Spurring additional learn on a number of open difficulties, this quantity highlights the usually implicit assumptions in laptop studying concepts that weren't totally understood some time past. The e-book additionally illustrates the industrial significance of cost-sensitive desktop studying via its assurance of the swift program advancements made through best businesses and educational examine labs.
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Extra resources for Cost-Sensitive Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Cost-Sensitive Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition) by Balaji Krishnapuram,Shipeng Yu,R. Bharat Rao